Building Trustworthy Data Foundations for AI

PROACTIVELY MANAGING COMPLIANCE

Building Trustworthy Data Foundations for AI Another approach to ensuring valid output from AI systems is to deploy a platform specially designed to manage data, risk, compliance, and privacy.

Daimler Truck North America (DTNA) wanted to use AI agents to provide early warning of supply chain problems, given the severe impact that such issues can create.

“Daimler has a million trucks on the road,” said Satyen Sangani, CEO and co-founder of Alation, “and if a truck breaks down, they guarantee delivery of parts within 24 hours.” The company developed AI agents in two categories: one group to serve as planners and another to carry out tasks. The agents monitor inventory, detect risks, and alert human planners about impending supply chain problems before small ones become big ones.

DTNA uses Alation to manage and govern the underlying data that informs the AI agents about the status of company resources and activities with respect to its manufacturing processes. Prior to launching this initiative, DTNA had also been using Alation to manage General Data Protection Regulation and other compliance requirements, finance-related matters, and assess data quality throughout the enterprise.

As a result of its reliance on Alation, DTNA is able to make evidence-based decisions across numerous enterprise functions, and Daimler has been forthcoming with its expressions of satisfaction about the role Alation is playing. Alation was founded to connect people with the high- quality data they need in order to do their jobs. The company first introduced a data catalog that combined machine learning with human curators. In the following years, Alation developed AI agents to manage and govern data and AI applications and, most recently, introduced the Alation Intelligence Operating System.

“Ultimately, our platform is designed to help organizations get reliable answers from AI,” explained Sangani. “The best way to launch an AI project is to start with a specific business problem, such as a compliance issue that has to be implemented. In such cases, it is usually clear what data is needed, and the outcomes can be tested.” One of the most difficult obstacles is that although customers may like the idea of AI, they don’t fully trust it and do not want to change the way they work. “People have legitimate reasons to be cautious,” agreed Sangani, “but when they work with a reliable system that proves its value over time, there can be real benefits to efficiency and ROI.”

GOVERNANCE AND ACCURACY FOR KNOWLEDGE MANAGEMENT

Knowledge management (KM) has been one of the major beneficiaries of AI. “Many have woken up to the fact that knowledge management as a discipline is fundamental and a good use of AI,” said Stephen Bedford, product director at ClearPeople. “However, provenance is increasingly important, and the output needs to be correctly sourced.” ClearPeople’s AtlasFuse is a KM platform designed for Microsoft 365 users. It was developed based on the company’s professional services experience in KM to address the lack of an existing KM platform for Microsoft 365.

“All of the security of Microsoft 365 is present, but we add several layers of governance,” continued Bedford. “The user can configure AI settings such as the model’s ‘temperature,’ which controls how deterministic or creative the responses are, as well as how restrictive the search should be.”

Typically, the user would limit the search to content in a particular collection, or only retrieve content within a certain timeframe or just the final approved version of a document. Users can also identify the owners of the information to allow for direct verification.

“Copilot alone can come up with an authoritative-sounding answer that is wrong,” he noted. “AtlasFuse can restrict the AI search, instructing it not to provide an answer if the specified content does not contain it.”

At the Royal College of Psychiatrists in England, one of the tests students must take requires an interview with an individual taking the role of the patient to ensure that the students are interacting appropriately.

In order to improve the performance of those who did not pass, feedback was provided by two evaluators who analyzed the conversation and actions of the student. AtlasFuse was trained to do these reviews, with issues of concern defined by subject matter experts, and now the time required for the feedback process has been reduced by 80%.

The Royal College of Psychiatrists methodically validated the system before deploying it by comparing its assessments against expert-reviewed, trusted reference answers across a representative set of examinations, refining the prompts and evaluation criteria until the AI consistently aligned with the expected outcomes.

In addition, the college can now provide useful feedback to all test takers, not just those who failed, which was not previously possible from a time and resources perspective.  Building reliable, governed, and high-quality data foundations that reduce hallucinations and improve trust in AI-driven actions and outputs is an achievable goal. However, it requires a combination of planning, vision, and technical expertise. We are still on the learning curve, but the potential is significant and worth the effort.